Luan, Xin-ZeXin-ZeLuanLiang, YongYongLiangLiu, ChengChengLiuProf. LEUNG Kwong SakChan, Tak-MingTak-MingChanXu , Zong-BenZong-BenXuZhang, HaiHaiZhang2023-03-162023-03-162014Soft Computing, 2014, vol. 18, pp.143–1521432-76431433-7479http://hdl.handle.net/20.500.11861/7501Nowadays, a series of methods are based on a L 1 penalty to solve the variable selection problem for a Cox’s proportional hazards model. In 2010, Xu et al. have proposed a L 1/2 regularization and proved that the L 1/2 penalty is sparser than the L 1 penalty in linear regression models. In this paper, we propose a novel shooting method for the L 1/2 regularization and apply it on the Cox model for variable selection. The experimental results based on comprehensive simulation studies, real Primary Biliary Cirrhosis and diffuse large B cell lymphoma datasets show that the L 1/2 regularization shooting method performs competitively.enVariable SelectionCox ModelLassoL 1/2 Regularization Shooting AlgorithmPeer Reviewed Journal Article10.1007/s00500-013-1042-6